Introduction to Cytoscape and it’s Apps -
Session 1
Akshay Bhat
Last modified: 06 Mar 2024
Part A:
Course Overview
The workshop is designed to give you an introduction on using
Cytoscape – a network-based software application to
perform bioinformatics pathway enrichment analysis. The particular
use-case is that of RNA-seq data, but many of the concepts are used for
other types of analysis. There are several reasons why we might want to
use Cytoscpae in Bioinformatics:- * You can be a
non-programmer and want to analyse your biological datasets generated by
“high-throughput sequencing”.
Cytoscape packages.
The packages within Cytoscape provides a user complete support for
different types of “-Omics” datasets.
e.g. many NGS tools are developed for a Unix envi3
+—————–speed than we have on our own machine
Some analyses take too long, and whilst they are running everything
else on our machine will be slow
Eventually you will want to use “High-performance computing” (HPC) -
but this requires some background knowledge before you can use it
Creating Network Graphs with Cytoscape
Cytoscape is a tool for viewing and analysing
networks (meaning, in this case, any group of entities that are
connected in some way). Cytoscape is not too hard to
use, but it won’t make much sense unless you have a sense of some basic
network analysis vocabulary and concepts. A glossary is made below for
basic understanding of introduction to network graphs.
These tutorials provide a basic introduction to using
Cytoscape to conduct network analysis of biological
data. They’re intended to be used with the data I collected about
bladder cancer muscle invasive phenotype. However, anyone should be able
to follow along.
Here’s what’s included:
Creating a basic Cytoscape network (that’s this
tutorial)
Importing a node list and working with node attributes
Selecting parts of your network
Publishing your network diagram
In addition, you’ll likely need to manipulate your data in order to
work with it. To that end, I’ve provided two tutorials to help you set
up your data:
Preparing Data 1: Making an Edge List.
Preparing Data 2: Making a Node List from an Edge
List.
Setting up your data
The most important thing to understand about Cytoscape (or, really,
most network analysis tools) is the format in which you need to
structure your data. At its most basic, Cytoscape wants a spreadsheet
containing two columns. The objects in the first column should be
connected in some way to the objects in the second column. This is
called an edge list. In the example below, my edge list
contains two kinds of things: geneA and
geneB. The columns could be reordered so that the geneB
column precedes the geneA column; it doesn’t matter. All that matters
here is that each row in my spreadsheet describes a connection between
two entities.
Other kinds of relationships you could describe in an edge
list: - Gene A appears in a pathway with
Gene B. - Gene A activates
Pathway A. - Gene B
suppresses Pathway A. … and on and on. The
important thing here is that your edge list should contain a list of
relationships between, at most, two kinds of entities (e.g., geneA and
geneB, features and pathways). The nature of those relationships can
differ. For example, Gene A could have up-regulated Pathway A, while
Gene B down-regulates Pathway A. But there should be two different kinds
of things, at most, described in two columns.
If you need specific guidance on setting up your edge list, see
Preparing Data 1: Making an Edge List. This is just an example using
actors and films as data. You can save your spreadsheet as an Excel
document (with the file extension .xls) or its generic equivalent, a CSV
(with the file extension .csv). Cytoscape can interpret both of these
formats.
Open up Cytoscape. You should be greeted with a welcome pane that
looks something like the one below. (If not, read on for instructions on
how to accomplish the same thing without the welcome screen.)
Start a new session From Network File. Somewhat
confusingly, Cytoscape considers your edge list a “network file.” I
guess it is a network, so maybe it’s not that confusing. Anyway, press
the From Network File button and select the edge list
you’ve prepared. You can also directly drag your .CSV file into
Cytoscape network.
If you don’t see that welcome screen…
no big deal. Someone might have disabled it, but you can accomplish
the same thing by clicking on the Import Network from
File button (circled below) and selecting your edge list.
Help Cytoscape understand your data
The screen that pops up after you imported your edge list might be
the most initially confusing part of Cytoscape, although it’s no problem
once you figure out what it wants. You need to tell Cytoscape which
parts of your data constitute the entities in your network diagram.
Cytoscape hasn’t understood right away that you want
a network composed of geneA and geneB
nodes. You can tell that’s the case because the icon that appears next
to the words gene symbol A and Interaction
Type looks sort of like a document, which means that Cytoscape
thinks that you’ve fed it a list of edge attributes,
not edges themselves. Edge attributes can come in handy, as I’ll explain
below, but that’s not what you’ve provided Cytoscape at the moment.
We need to tell Cytoscape that the edge list we’ve
provided contains Sources in one column and
Targets in another. It doesn’t matter, in this case,
which column you designate a source and which column
you designate a target; it’s just that one column has
to be one and the other has to be the other. (If our network was
directed, this would matter, but that’s more
information than you need right now.)
OK, let’s tell
Cytoscape how to interpret our data. Click on the arrow next to the word
Gene Symbol A and from the menu that appears, select
the green circle. Cryptically, this icon means Source.
For the Gene Symbol B column, do the same thing, but
this time select the red bullseye, which means Target.
When you’ve changed both columns, click
OK.
You have a (very confusing) network!
If the previous steps worked properly, you should have a network of
connected nodes. If you have more than 100 or so, though, your network
will look a lot like a hairball. We’ll talk about ways to make your
network graph more legible in subsequent steps. For now, note
that you can zoom in on parts of your network using the magnifying
glasses or your mouse. On a Mac, you can move your canvas around if you
hold down the command key and drag. You can also search
for individual nodes by using the search box at the upper right of the
window. (Note that Cytoscape doesn’t zoom to the node you
searched for right away; it just highlights it in yellow. You may have
to zoom out to see the selected node.).
Switch up your style
One of the easiest ways to change the look of your network diagram is
to switch the style using one of Cytoscape’s built-in options. To do
that, click the Style tab on the control panel and then
choose one of the options with which you’re presented. I don’t know if I
love any of them, but some are more legible than others.
Change your layout
Sometimes networks are more legible if you change the arrangement of
the nodes. You can do this by switching the layout. Click on
Layout from Cytoscape’s menu bar and select one of the
layout options. Experiment with various layouts and notice how
drastically your network diagram changes.
Confoundingly, each of these wildly different network layouts displays
the same basic information. How can this be? Simple: In most network
graphs, the proximity of two nodes doesn’t indicate anything except
legibility. This can be really confusing, because cognitively, we expect
proximity to convey meaningful information. It just doesn’t, though, in
most network diagrams. The presence or absence of an edge means
something, and color and size often do, too. But proximity generally
doesn’t.
Customize your style
You can make changes to many visual attributes of your network graph
from Cytoscape’s control panel. Once you’ve selected the
Style tab, look at the bottom of the control panel.
You’ll see that three additional buttons allow you to customize the look
of the nodes, the edges, and the
network as a whole.
Looking at the
Node style pane, you’ll see that we can alter many
aspects of our nodes, including their border, the color, height, labels,
etc. But what do the three columns, labeled Def., Map.,
and Byp. mean?
Def. stands for default: the default
visual attribute of a node. If you click on the default fill color for
your network diagram, for example (in the image below, it’s a blue
square), you’ll see that you can swap out the colors of your network’s
nodes.
Byp. stands for
bypass, and it allows you to apply a style to a group
of nodes that you select.
The Map.
(mapping) column allows you to control the visual features of your nodes
according to their properties. For example, let’s say you’d like all of
the films from a particular studio to appear in pink. You can do that by
mapping that attribute to a particular color. Or, let’s say you’d like
those nodes with more connections to appear larger. You can do that with
the mapping control.
The problem is, we don’t have
any of that information currently included in our network. So in the
next tutorial, we’ll look at how to load up attributes
for our nodes.
Cytoscape provides a number of ways to save results and
visualizations: As a session: File → Save Session, File → Save
Session As…
As an image: File → Export → Network to Image…
To the web: File → Export → Network to
Web Page… To a public repository: File →
Export → Network to NDEx As a graph format file:
File → Export → Network to File.
Cytoscape is an open source software platform for integrating,
visualizing, and analysing measurement data in the context of
networks.
This protocol describes a network analysis workflow in Cytoscape for
differentially expressed genes from an RNA-Seq experiment. Overall
workflow:
• Finding a set of differentially expressed genes. •
Retrieving relevant networks from public databases. •
Integration and visualization of experimental data. • Network
functional enrichment analysis. • Exporting network
visualizations.
Setup
Install the (stringApp) from the
Cytoscape App Store, or via Apps → App Store → Show App
Store.
OR
Just visit the Cytoscape App store and
install/download it from there.
Experimental Data
For this exercise, we will use a dataset comparing transcriptomic
differences between bladder cancer and normal tissue. The study has been
published by Radvanyi F et al., and we will get a summarized dataset
with fold change and p-value from the EBI Gene Expression
Atlas. Array-express ID is E-MTAB-1940.
• Download the data: Transcriptomic analysis of bladder cancer
reveals convergent molecular pathology. (Here..!)
• To open the tsv datafile in Excel, first launch Excel and
open a blank workbook. Next, go to Data → Get External Data →
Import Text File…. • In the import wizard, select
Delimited and in the next step select Tab. •
In the third step, you can select the Data Format for
every column. The file has 4 columns of data: Gene ID, Gene
Name, fold change and p-value. Make sure to change the
format for the second column, Gene Name, to Text. You will have
to scroll to the right to see the second column. • Click
Finish to complete the import.
Experimental Data
We are going to define a set of up-regulated genes from the full
dataset by filtering for fold change and p-value.
•
Select the row containing data value headers (row 4) and select
Data → Filter. • In the drop-down for the fold
change column, set a filter for fold change greater than 2. This should
result in 263 genes. • Next, one would
normally filter out non-significant changes by filtering on the p-value
as well, for example setting p-value less than 0.05. But in this case,
all genes with a fold change greater than 2 already meet that
cutoff. • With the filter active, select and copy all entries
in the Gene Name column.
Retrieve Networks from STRING
To identify a relevant network, we will use the
STRING database to find a network relevant to the list
of up-regulated genes.
• Launch Cytoscape. In the
Network Search bar at the top of the Network
Panel, select STRING protein query from the
drop-down, and paste in the list of 263 up-regulated genes. •
Open the options panel and confirm you are searching Homo
sapiens with a Confidence cutoff of 0.40 and
0 Maximum additional interactors. • Click the
search icon to search. If any of the search terms are
ambiguous, a Resolve Ambiguous Terms dialog will
appear. Click Import to continue with the import using
the default choices. The resulting network will load automatically, and
should have around 173 nodes.
STRING Network Up-Regulated Genes
The resulting network contains up-regulated genes recognized by
STRING, and interactions between them with a confidence score of 0.4 or
greater.
The networks consist of one large connected component, several
smaller networks, and some unconnected nodes. We will use only the
largest connected component for the rest of the tutorial.
• To select the largest connected component, select Select →
Nodes → Largest subnetwork. • Select File →
New Network → From Selected Nodes, All Edges.
Data Integration
Next we will import the RNA-Seq data and use them to create a
visualization.
• Load the downloaded E-MTAB-1940-query-results.tsv
file under File menu by selecting Import → Table from
File….. Alternatively, drag and drop the data file directly
onto the Node Table. • In Advanced Options…,
in the Ignore Lines Starting With field, enter #, to
exclude the additional lines at the beginning of the data file.
• Select the query term column as the Key
column for Network and select the Gene Name
column as the key column by clicking on the header and selecting the key
symbol. • Click OK to import. Two new columns
of data will be added to the Node Table.
Visualization
Next, we will create a visualization of the imported data on the
network. For more detailed information on data visualization, see the
Visualizing Data tutorial.
• In the Style tab of the Control
Panel, switch the style from STRING style to
default in the drop-down at the top. • Change
the default node Shape to ellipse and
check Lock node width and height. • Set the
default node Size to 50. •
Set the default node Fill Color to light
gray. • Set the default Border Width
to 2, and make the default Border Paint dark
gray.
• For node Fill Color,
create a continuous mapping for ‘NMIBC’ vs ‘normal’
.foldChange. • Double-click the color mapping to open the
Continuous Mapping Editor and click the Current
Palette. Select the ColorBrewer yellow-orange-red
shades gradient. • Finally, for node
Label, set a passthrough mapping for display
name. • Save your new visualization under Copy
Style… in the Options menu of the
Style interface, and name it de genes up.
Apply the Prefuse Force Directed layout by clicking the
Apply Preferred Layout button in the toolbar. The
network will now look something like this:
Exercise
STRING Enrichment
The STRING app has built-in enrichment analysis functionality, which
includes enrichment for Gene Ontology, InterPro, KEGG Pathways, and
PFAM.
• Using the STRING tab of the Results Panel, click the
Functional Enrichment button. Keep the default
settings. What do you see.
• When the enrichment analysis is complete, a new
tab titled STRINGEnrichment will open
in the Table Panel.
The STRING app
includes several options for filtering and displaying the enrichment
results. The features are all available at the top of the STRING
Enrichment tab. Filter the table to only show GO
Biological Process.
Answer
• At the top left of the STRING enrichment tab, click the filter icon
. Select GO Biological Process and check the
Remove redundant terms check-box. Then click
OK. • Next, add a split donut chart to the
nodes representing the top terms by clicking on • Explore
custom settings via in the top right of the STRING enrichment
tab.
Exporting Networks
Cytoscape provides a number of ways to save results and
visualizations:
• As a session: File → Save Session, File → Save Session
As… • As an image: File → Export → Network to
Image… • As a graph format file: File →
Export → Network to File. Formats: CX JSON / CX2 JSON Cytoscape.js JSON GraphML
Save in any of the formats and be ready for publishing.
